Knowledge-graph biased classification for data

ABSTRACT

A method for classifying an object includes applying multiple confidence values to multiple objects. The method also includes determining a metric based on the multiple confidence values. The method further includes determining a classification of a first object from the multiple objects based on a knowledge-graph when the metric is above a threshold.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/039,329 entitled “KNOWLEDGE-GRAPHBIASED CLASSIFICATION FOR DATA,” filed on Aug. 19, 2014, the disclosureof which is expressly incorporated by reference herein in its entirety.

BACKGROUND

Field

Certain aspects of the present disclosure generally relate to neuralsystem engineering and, more particularly, to knowledge-graph biasedclassification.

Background

An artificial neural network, which may comprise an interconnected groupof artificial neurons (i.e., neuron models), is a computational deviceor represents a method to be performed by a computational device.Artificial neural networks may have corresponding structure and/orfunction in biological neural networks. However, artificial neuralnetworks may provide innovative and useful computational techniques forcertain applications in which traditional computational techniques arecumbersome, impractical, or inadequate. Because artificial neuralnetworks can infer a function from observations, such networks areparticularly useful in applications where the complexity of the task ordata makes the design of the function by conventional techniquesburdensome. Depending on the complexity of the data and/or the networkarchitecture, the neural network may not use the co-occurrences ofpatterns for object recognition. Thus, it is desirable to provide aneuromorphic receiver to classify objects in data based onknowledge-graph biasing.

SUMMARY

In one aspect of the present disclosure, a method for classifying anobject is disclosed. The method includes applying multiple confidencevalues to multiple objects. The method also includes determiningH ametric based on the multiple confidence values. The method furtherincludes determining a classification of a first object from themultiple objects based on a knowledge-graph when the metric is above athreshold.

Another aspect of the present disclosure is directed to an apparatusincluding means for applying multiple confidence values to multipleobjects. The apparatus also includes means for determining a metricbased on the multiple confidence values. The apparatus further includesmeans for determining a classification of a first object from themultiple objects based on a knowledge-graph when the metric is above athreshold.

In another aspect of the present disclosure, a computer program productfor classifying an object is disclosed. The computer program product hasa non-transitory computer-readable medium with non-transitory programcode recorded thereon. The program code includes program code to applymultiple confidence values to multiple objects. The program code alsoincludes program code to determine a metric based on the multipleconfidence values. The program code further includes program code todetermine a classification of a first object from the multiple objectsbased on a knowledge-graph when the metric is above a threshold.

Another aspect of the present disclosure is directed to an apparatus forclassifying an object having a memory and one or more processors coupledto the memory. The processor(s) is configured to apply multipleconfidence values to multiple objects. The processor(s) is alsoconfigured to determine a metric based on the multiple confidencevalues. The processor(s) is further configured to determine aclassification of a first object from the multiple objects based on aknowledge-graph when the metric is above a threshold.

Additional features and advantages of the disclosure will be describedbelow. It should be appreciated by those skilled in the art that thisdisclosure may be readily utilized as a basis for modifying or designingother structures for carrying out the same purposes of the presentdisclosure. It should also be realized by those skilled in the art thatsuch equivalent constructions do not depart from the teachings of thedisclosure as set forth in the appended claims. The novel features,which are believed to be characteristic of the disclosure, both as toits organization and method of operation, together with further objectsand advantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example network of neurons in accordance withcertain aspects of the present disclosure.

FIG. 2 illustrates an example of a processing unit (neuron) of acomputational network (neural system or neural network) in accordancewith certain aspects of the present disclosure.

FIG. 3 illustrates an example of spike-timing dependent plasticity(STDP) curve in accordance with certain aspects of the presentdisclosure.

FIG. 4 illustrates an example of a positive regime and a negative regimefor defining behavior of a neuron model in accordance with certainaspects of the present disclosure.

FIG. 5 illustrates an example of an image captured for processing by anobject recognition system.

FIG. 6 illustrates an example of a knowledge-graph according to anaspect of the present disclosure.

FIG. 7 illustrates an example implementation of designing a neuralnetwork using a general-purpose processor in accordance with certainaspects of the present disclosure.

FIG. 8 illustrates an example implementation of designing a neuralnetwork where a memory may be interfaced with individual distributedprocessing units in accordance with certain aspects of the presentdisclosure.

FIG. 9 illustrates an example implementation of designing a neuralnetwork based on distributed memories and distributed processing unitsin accordance with certain aspects of the present disclosure.

FIG. 10 illustrates an example implementation of a neural network inaccordance with certain aspects of the present disclosure.

FIG. 11 is a flow diagram illustrating a method for classifying anobject in accordance with an aspect of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. However, it will beapparent to those skilled in the art that these concepts may bepracticed without these specific details. In some instances, well-knownstructures and components are shown in block diagram form in order toavoid obscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the disclosure is intended to cover any aspect of thedisclosure, whether implemented independently of or combined with anyother aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth. In addition, the scope of the disclosure is intended to coversuch an apparatus or method practiced using other structure,functionality, or structure and functionality in addition to or otherthan the various aspects of the disclosure set forth. It should beunderstood that any aspect of the disclosure disclosed may be embodiedby one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to differenttechnologies, system configurations, networks and protocols, some ofwhich are illustrated by way of example in the figures and in thefollowing description of the preferred aspects. The detailed descriptionand drawings are merely illustrative of the disclosure rather thanlimiting, the scope of the disclosure being defined by the appendedclaims and equivalents thereof.

An Example Neural System, Training And Operation

FIG. 1 illustrates an example artificial neural system 100 with multiplelevels of neurons in accordance with certain aspects of the presentdisclosure. The neural system 100 may have a level of neurons 102connected to another level of neurons 106 through a network of synapticconnections 104 (i.e., feed-forward connections). For simplicity, onlytwo levels of neurons are illustrated in FIG. 1, although fewer or morelevels of neurons may exist in a neural system. It should be noted thatsome of the neurons may connect to other neurons of the same layerthrough lateral connections. Furthermore, some of the neurons mayconnect back to a neuron of a previous layer through feedbackconnections.

As illustrated in FIG. 1, each neuron in the level 102 may receive aninput signal 108 that may be generated by neurons of a previous level(not shown in FIG. 1). The signal 108 may represent an input current ofthe level 102 neuron. This current may be accumulated on the neuronmembrane to charge a membrane potential. When the membrane potentialreaches its threshold value, the neuron may fire and generate an outputspike to be transferred to the next level of neurons (e.g., the level106). In some modeling approaches, the neuron may continuously transfera signal to the next level of neurons. This signal is typically afunction of the membrane potential. Such behavior can be emulated orsimulated in hardware and/or software, including analog and digitalimplementations such as those described below.

In biological neurons, the output spike generated when a neuron fires isreferred to as an action potential. This electrical signal is arelatively rapid, transient, nerve impulse, having an amplitude ofroughly 100 mV and a duration of about 1 ms. In a particular embodimentof a neural system having a series of connected neurons (e.g., thetransfer of spikes from one level of neurons to another in FIG. 1),every action potential has basically the same amplitude and duration,and thus, the information in the signal may be represented only by thefrequency and number of spikes, or the time of spikes, rather than bythe amplitude. The information carried by an action potential may bedetermined by the spike, the neuron that spiked, and the time of thespike relative to other spike or spikes. The importance of the spike maybe determined by a weight applied to a connection between neurons, asexplained below.

The transfer of spikes from one level of neurons to another may beachieved through the network of synaptic connections (or simply“synapses”) 104, as illustrated in FIG. 1. Relative to the synapses 104,neurons of level 102 may be considered presynaptic neurons and neuronsof level 106 may be considered postsynaptic neurons. The synapses 104may receive output signals (i.e., spikes) from the level 102 neurons andscale those signals according to adjustable synaptic weights w₁^((i,i+1)), . . . , w_(P) ^((i,i+1)) where P is a total number ofsynaptic connections between the neurons of levels 102 and 106 and i isan indicator of the neuron level. In the example of FIG. 1, i representsneuron level 102 and i+1 represents neuron level 106. Further, thescaled signals may be combined as an input signal of each neuron in thelevel 106. Every neuron in the level 106 may generate output spikes 110based on the corresponding combined input signal. The output spikes 110may be transferred to another level of neurons using another network ofsynaptic connections (not shown in FIG. 1).

Biological synapses can mediate either excitatory or inhibitory(hyperpolarizing) actions in postsynaptic neurons and can also serve toamplify neuronal signals. Excitatory signals depolarize the membranepotential (i.e., increase the membrane potential with respect to theresting potential). If enough excitatory signals are received within acertain time period to depolarize the membrane potential above athreshold, an action potential occurs in the postsynaptic neuron. Incontrast, inhibitory signals generally hyperpolarize (i.e., lower) themembrane potential. Inhibitory signals, if strong enough, can counteractthe sum of excitatory signals and prevent the membrane potential fromreaching a threshold. In addition to counteracting synaptic excitation,synaptic inhibition can exert powerful control over spontaneously activeneurons. A spontaneously active neuron refers to a neuron that spikeswithout further input, for example due to its dynamics or a feedback. Bysuppressing the spontaneous generation of action potentials in theseneurons, synaptic inhibition can shape the pattern of firing in aneuron, which is generally referred to as sculpturing. The varioussynapses 104 may act as any combination of excitatory or inhibitorysynapses, depending on the behavior desired.

The neural system 100 may be emulated by a general purpose processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components, a software module executed by a processor,or any combination thereof. The neural system 100 may be utilized in alarge range of applications, such as image and pattern recognition,machine learning, motor control, and alike. Each neuron in the neuralsystem 100 may be implemented as a neuron circuit. The neuron membranecharged to the threshold value initiating the output spike may beimplemented, for example, as a capacitor that integrates an electricalcurrent flowing through it.

In an aspect, the capacitor may be eliminated as the electrical currentintegrating device of the neuron circuit, and a smaller memristorelement may be used in its place. This approach may be applied in neuroncircuits, as well as in various other applications where bulkycapacitors are utilized as electrical current integrators. In addition,each of the synapses 104 may be implemented based on a memristorelement, where synaptic weight changes may relate to changes of thememristor resistance. With nanometer feature-sized memristors, the areaof a neuron circuit and synapses may be substantially reduced, which maymake implementation of a large-scale neural system hardwareimplementation more practical.

Functionality of a neural processor that emulates the neural system 100may depend on weights of synaptic connections, which may controlstrengths of connections between neurons. The synaptic weights may bestored in a non-volatile memory in order to preserve functionality ofthe processor after being powered down. In an aspect, the synapticweight memory may be implemented on a separate external chip from themain neural processor chip. The synaptic weight memory may be packagedseparately from the neural processor chip as a replaceable memory card.This may provide diverse functionalities to the neural processor, wherea particular functionality may be based on synaptic weights stored in amemory card currently attached to the neural processor.

FIG. 2 illustrates an exemplary diagram 200 of a processing unit (e.g.,a neuron or neuron circuit) 202 of a computational network (e.g., aneural system or a neural network) in accordance with certain aspects ofthe present disclosure. For example, the neuron 202 may correspond toany of the neurons of levels 102 and 106 from FIG. 1. The neuron 202 mayreceive multiple input signals 204 ₁-204 _(N), which may be signalsexternal to the neural system, or signals generated by other neurons ofthe same neural system, or both. The input signal may be a current, aconductance, a voltage, a real-valued, and/or a complex-valued. Theinput signal may comprise a numerical value with a fixed-point or afloating-point representation. These input signals may be delivered tothe neuron 202 through synaptic connections that scale the signalsaccording to adjustable synaptic weights 206 ₁-206 _(N) (W₁₋W_(N)),where N may be a total number of input connections of the neuron 202.

The neuron 202 may combine the scaled input signals and use the combinedscaled inputs to generate an output signal 208 (i.e., a signal Y). Theoutput signal 208 may be a current, a conductance, a voltage, areal-valued and/or a complex-valued. The output signal may be anumerical value with a fixed-point or a floating-point representation.The output signal 208 may be then transferred as an input signal toother neurons of the same neural system, or as an input signal to thesame neuron 202, or as an output of the neural system.

The processing unit (neuron) 202 may be emulated by an electricalcircuit, and its input and output connections may be emulated byelectrical connections with synaptic circuits. The processing unit 202and its input and output connections may also be emulated by a softwarecode. The processing unit 202 may also be emulated by an electriccircuit, whereas its input and output connections may be emulated by asoftware code. In an aspect, the processing unit 202 in thecomputational network may be an analog electrical circuit. In anotheraspect, the processing unit 202 may be a digital electrical circuit. Inyet another aspect, the processing unit 202 may be a mixed-signalelectrical circuit with both analog and digital components. Thecomputational network may include processing units in any of theaforementioned forms. The computational network (neural system or neuralnetwork) using such processing units may be utilized in a large range ofapplications, such as image and pattern recognition, machine learning,motor control, and the like.

During the course of training a neural network, synaptic weights (e.g.,the weights w₁ ^((i,i+1)), . . . , w_(P) ^((i,i+1)) from FIG. 1 and/orthe weights 206 ₁-206 _(N) from FIG. 2) may be initialized with randomvalues and increased or decreased according to a learning rule. Thoseskilled in the art will appreciate that examples of the learning ruleinclude, but are not limited to the spike-timing-dependent plasticity(STDP) learning rule, the Hebb rule, the Oja rule, theBienenstock-Copper-Munro (BCM) rule, etc. In certain aspects, theweights may settle or converge to one of two values (i.e., a bimodaldistribution of weights). This effect can be utilized to reduce thenumber of bits for each synaptic weight, increase the speed of readingand writing from/to a memory storing the synaptic weights, and to reducepower and/or processor consumption of the synaptic memory.

Synapse Type

In hardware and software models of neural networks, the processing ofsynapse related functions can be based on synaptic type. Synapse typesmay be non-plastic synapses (no changes of weight and delay), plasticsynapses (weight may change), structural delay plastic synapses (weightand delay may change), fully plastic synapses (weight, delay andconnectivity may change), and variations thereupon (e.g., delay maychange, but no change in weight or connectivity). The advantage ofmultiple types is that processing can be subdivided. For example,non-plastic synapses may not use plasticity functions to be executed (orwaiting for such functions to complete). Similarly, delay and weightplasticity may be subdivided into operations that may operate togetheror separately, in sequence or in parallel. Different types of synapsesmay have different lookup tables or formulas and parameters for each ofthe different plasticity types that apply. Thus, the methods wouldaccess the relevant tables, formulas, or parameters for the synapse'stype.

There are further implications of the fact that spike-timing dependentstructural plasticity may be executed independently of synapticplasticity. Structural plasticity may be executed even if there is nochange to weight magnitude (e.g., if the weight has reached a minimum ormaximum value, or it is not changed due to some other reason) sstructural plasticity (i.e., an amount of delay change) may be a directfunction of pre-post spike time difference. Alternatively, structuralplasticity may be set as a function of the weight change amount or basedon conditions relating to bounds of the weights or weight changes. Forexample, a synapse delay may change only when a weight change occurs orif weights reach zero but not if they are at a maximum value. However,it may be advantageous to have independent functions so that theseprocesses can be parallelized reducing the number and overlap of memoryaccesses.

Determination of Synaptic Plasticity

Neuroplasticity (or simply “plasticity”) is the capacity of neurons andneural networks in the brain to change their synaptic connections andbehavior in response to new information, sensory stimulation,development, damage, or dysfunction. Plasticity is important to learningand memory in biology, as well as for computational neuroscience andneural networks. Various forms of plasticity have been studied, such assynaptic plasticity (e.g., according to the Hebbian theory),spike-timing-dependent plasticity (STDP), non-synaptic plasticity,activity-dependent plasticity, structural plasticity and homeostaticplasticity.

STDP is a learning process that adjusts the strength of synapticconnections between neurons. The connection strengths are adjusted basedon the relative timing of a particular neuron's output and receivedinput spikes (i.e., action potentials). Under the STDP process,long-term potentiation (LTP) may occur if an input spike to a certainneuron tends, on average, to occur immediately before that neuron'soutput spike. Then, that particular input is made somewhat stronger. Onthe other hand, long-term depression (LTD) may occur if an input spiketends, on average, to occur immediately after an output spike. Then,that particular input is made somewhat weaker, and hence the name“spike-timing-dependent plasticity.” Consequently, inputs that might bethe cause of the postsynaptic neuron's excitation are made even morelikely to contribute in the future, whereas inputs that are not thecause of the postsynaptic spike are made less likely to contribute inthe future. The process continues until a subset of the initial set ofconnections remains, while the influence of all others is reduced to aninsignificant level.

Because a neuron generally produces an output spike when many of itsinputs occur within a brief period (i.e., being cumulative sufficient tocause the output), the subset of inputs that typically remains includesthose that tended to be correlated in time. In addition, because theinputs that occur before the output spike are strengthened, the inputsthat provide the earliest sufficiently cumulative indication ofcorrelation will eventually become the final input to the neuron.

The STDP learning rule may effectively adapt a synaptic weight of asynapse connecting a presynaptic neuron to a postsynaptic neuron as afunction of time difference between spike time t_(pre) of thepresynaptic neuron and spike time t_(post) of the postsynaptic neuron(i.e., t=t_(post)−t_(pre)). A typical formulation of the STDP is toincrease the synaptic weight (i.e., potentiate the synapse) if the timedifference is positive (the presynaptic neuron fires before thepostsynaptic neuron), and decrease the synaptic weight (i.e., depressthe synapse) if the time difference is negative (the postsynaptic neuronfires before the presynaptic neuron).

In the STDP process, a change of the synaptic weight over time may betypically achieved using an exponential decay, as given by:

$\begin{matrix}{{\Delta\;{w(t)}} = \left\{ {\begin{matrix}{{{a_{+}e^{{- t}/k_{+}}} + \mu},{t > 0}} \\{{a_{-}e^{t/k_{-}}},{t < 0}}\end{matrix},} \right.} & (1)\end{matrix}$where and k₊ and k_τ_(sign(Δt)) are time constants for positive andnegative time difference, respectively, α⁻ and α⁻ are correspondingscaling magnitudes, and μ is an offset that may be applied to thepositive time difference and/or the negative time difference.

FIG. 3 illustrates an exemplary diagram 300 of a synaptic weight changeas a function of relative timing of presynaptic and postsynaptic spikesin accordance with the STDP. If a presynaptic neuron fires before apostsynaptic neuron, then a corresponding synaptic weight may beincreased, as illustrated in a portion 302 of the graph 300. This weightincrease can be referred to as an LTP of the synapse. It can be observedfrom the graph portion 302 that the amount of LTP may decrease roughlyexponentially as a function of the difference between presynaptic andpostsynaptic spike times. The reverse order of firing may reduce thesynaptic weight, as illustrated in a portion 304 of the graph 300,causing an LTD of the synapse.

As illustrated in the graph 300 in FIG. 3, a negative offset μ may beapplied to the LTP (causal) portion 302 of the STDP graph. A point ofcross-over 306 of the x-axis (y=0) may be configured to coincide withthe maximum time lag for considering correlation for causal inputs fromlayer i-1. In the case of a frame-based input (i.e., an input that is inthe form of a frame of a particular duration comprising spikes orpulses), the offset value μ can be computed to reflect the frameboundary. A first input spike (pulse) in the frame may be considered todecay over time either as modeled by a postsynaptic potential directlyor in terms of the effect on neural state. If a second input spike(pulse) in the frame is considered correlated or relevant to aparticular time frame, then the relevant times before and after theframe may be separated at that time frame boundary and treateddifferently in plasticity terms by offsetting one or more parts of theSTDP curve such that the value in the relevant times may be different(e.g., negative for greater than one frame and positive for less thanone frame). For example, the negative offset μ may be set to offset LTPsuch that the curve actually goes below zero at a pre-post time greaterthan the frame time and it is thus part of LTD instead of LTP.

Neuron Models and Operation

There are some general principles for designing a useful spiking neuronmodel. A good neuron model may have rich potential behavior in terms oftwo computational regimes: coincidence detection and functionalcomputation. Moreover, a good neuron model should have two elements toallow temporal coding: arrival time of inputs affects output time andcoincidence detection can have a narrow time window. Finally, to becomputationally attractive, a good neuron model may have a closed-formsolution in continuous time and stable behavior including nearattractors and saddle points. In other words, a useful neuron model isone that is practical and that can be used to model rich, realistic andbiologically-consistent behaviors, as well as be used to both engineerand reverse engineer neural circuits.

A neuron model may depend on events, such as an input arrival, outputspike or other event whether internal or external. To achieve a richbehavioral repertoire, a state machine that can exhibit complexbehaviors may be desired. If the occurrence of an event itself, separatefrom the input contribution (if any), can influence the state machineand constrain dynamics subsequent to the event, then the future state ofthe system is not only a function of a state and input, but rather afunction of a state, event, and input.

In an aspect, a neuron n may be modeled as a spikingleaky-integrate-and-fire neuron with a membrane voltage v_(n)(t)governed by the following dynamics:

$\begin{matrix}{{\frac{{dv}_{n}(t)}{dt} = {{\alpha\;{v_{n}(t)}} + {\beta\;{\sum\limits_{m}{w_{m,n}{y_{m}\left( {t - {\Delta\; t_{m,n}}} \right)}}}}}},} & (2)\end{matrix}$where α and β are parameters, w_(m,n) is a synaptic weight for thesynapse connecting a presynaptic neuron m to a postsynaptic neuron n,and y_(m)(t) is the spiking output of the neuron m that may be delayedby dendritic or axonal delay according to Δt_(m,n) until arrival at theneuron n's soma.

It should be noted that there is a delay from the time when sufficientinput to a postsynaptic neuron is established until the time when thepostsynaptic neuron actually fires. In a dynamic spiking neuron model,such as Izhikevich's simple model, a time delay may be incurred if thereis a difference between a depolarization threshold v_(t) and a peakspike voltage v_(peak). For example, in the simple model, neuron somadynamics can be governed by the pair of differential equations forvoltage and recovery, i.e.:

$\begin{matrix}{{\frac{dv}{dt} = {\left( {{{k\left( {v - v_{t}} \right)}\left( {v - v_{r}} \right)} - u + I} \right)/C}},} & (3) \\{{\frac{du}{dt} = {a\left( {{b\left( {v - v_{r}} \right)} - u} \right)}},} & (4)\end{matrix}$where v is a membrane potential, u is a membrane recovery variable, k isa parameter that describes time scale of the membrane potential v, α isa parameter that describes time scale of the recovery variable u, b is aparameter that describes sensitivity of the recovery variable u to thesub-threshold fluctuations of the membrane potential v, v_(r) is amembrane resting potential, I is a synaptic current, and C is amembrane's capacitance. In accordance with this model, the neuron isdefined to spike when v>v_(peak).Hunzinger Cold Model

The Hunzinger Cold neuron model is a minimal dual-regime spiking lineardynamical model that can reproduce a rich variety of neural behaviors.The model's one- or two-dimensional linear dynamics can have tworegimes, wherein the time constant (and coupling) can depend on theregime. In the sub-threshold regime, the time constant, negative byconvention, represents leaky channel dynamics generally acting to returna cell to rest in a biologically-consistent linear fashion. The timeconstant in the supra-threshold regime, positive by convention, reflectsanti-leaky channel dynamics generally driving a cell to spike whileincurring latency in spike-generation.

As illustrated in FIG. 4, the dynamics of the model 400 may be dividedinto two (or more) regimes. These regimes may be called the negativeregime 402 (also interchangeably referred to as theleaky-integrate-and-fire (LIF) regime, not to be confused with the LIFneuron model) and the positive regime 404 (also interchangeably referredto as the anti-leaky-integrate-and-fire (ALIF) regime, not to beconfused with the ALIF neuron model). In the negative regime 402, thestate tends toward rest (v⁻) at the time of a future event. In thisnegative regime, the model generally exhibits temporal input detectionproperties and other sub-threshold behavior. In the positive regime 404,the state tends toward a spiking event (v_(s)). In this positive regime,the model exhibits computational properties, such as incurring a latencyto spike depending on subsequent input events. Formulation of dynamicsin terms of events and separation of the dynamics into these two regimesare fundamental characteristics of the model.

Linear dual-regime bi-dimensional dynamics (for states v and u) may bedefined by convention as:

$\begin{matrix}{{\tau_{\rho}\frac{dv}{dt}} = {v + q_{\rho}}} & (5) \\{{{{- \tau_{u}}\frac{du}{dt}} = {u + r}},} & (6)\end{matrix}$where q_(ρ) and r are the linear transformation variables for coupling.

The symbol ρ is used herein to denote the dynamics regime with theconvention to replace the symbol ρ with the sign “−” or “+” for thenegative and positive regimes, respectively, when discussing orexpressing a relation for a specific regime.

The model state is defined by a membrane potential (voltage) v andrecovery current u. In basic form, the regime is essentially determinedby the model state. There are subtle, but important aspects of theprecise and general definition, but for the moment, consider the modelto be in the positive regime 404 if the voltage v is above a threshold(v₊) and otherwise in the negative regime 402.

The regime-dependent time constants include τ_which is the negativeregime time constant, and τ_(|) which is the positive regime timeconstant. The recovery current time constant τ_(u) is typicallyindependent of regime. For convenience, the negative regime timeconstant τ⁻ is typically specified as a negative quantity to reflectdecay so that the same expression for voltage evolution may be used asfor the positive regime in which the exponent and τ_(|) will generallybe positive, as will be τ_(u).

The dynamics of the two state elements may be coupled at events bytransformations offsetting the states from their null-clines, where thetransformation variables are:q _(ρ)=−τ_(ρ) βu−v _(ρ)  (7)r=δ(v+ε),   (8)where δ, ε, β and v⁻, v₊ are parameters. The two values for v_(ρ) arethe base for reference voltages for the two regimes. The parameter v⁻ isthe base voltage for the negative regime, and the membrane potentialwill generally decay toward v⁻ in the negative regime. The parameter v₊is the base voltage for the positive regime, and the membrane potentialwill generally tend away from v₊ in the positive regime.

The null-clines for v and u are given by the negative of thetransformation variables q_(ρ) and r, respectively. The parameter δ is ascale factor controlling the slope of the u null-cline. The parameter εis typically set equal to −v⁻. The parameter β is a resistance valuecontrolling the slope of the v null-clines in both regimes. The τ_(ρ)time-constant parameters control not only the exponential decays, butalso the null-cline slopes in each regime separately.

The model may be defined to spike when the voltage v reaches a valuev_(s) . Subsequently, the state may be reset at a reset event (which maybe one and the same as the spike event):v={circumflex over (v)} ⁻  (9)u=u+Δu,   (10)where {circumflex over (v)}⁻ and Δu are parameters. The reset voltage{circumflex over (v)}⁻ is typically set to v⁻.

By a principle of momentary coupling, a closed form solution is possiblenot only for state (and with a single exponential term), but also forthe time to reach a particular state. The close form state solutionsare:

$\begin{matrix}{{v\left( {t + {\Delta\; t}} \right)} = {{\left( {{v(t)} + q_{\rho}} \right)e^{\frac{\Delta\; t}{\tau_{\rho}}}} - q_{\rho}}} & (11) \\{{u\left( {t + {\Delta\; t}} \right)} = {{\left( {{u(t)} + r} \right)e^{- \frac{\Delta\; t}{\tau_{u}}}} - {r.}}} & (12)\end{matrix}$

Therefore, the model state may be updated only upon events, such as aninput (presynaptic spike) or output (postsynaptic spike). Operations mayalso be performed at any particular time (whether or not there is inputor output).

Moreover, by the momentary coupling principle, the time of apostsynaptic spike may be anticipated so the time to reach a particularstate may be determined in advance without iterative techniques orNumerical Methods (e.g., the Euler numerical method). Given a priorvoltage state v₀, the time delay until voltage state v_(f) is reached isgiven by:

$\begin{matrix}{{\Delta\; t} = {\tau_{\rho}\log\;{\frac{v_{f} + q_{\rho\;}}{v_{0} + q_{\rho}}.}}} & (13)\end{matrix}$

If a spike is defined as occurring at the time the voltage state vreaches v_(s), then the closed-form solution for the amount of time, orrelative delay, until a spike occurs as measured from the time that thevoltage is at a given state v is:

$\begin{matrix}{{\Delta\; t_{S}} = \left\{ \begin{matrix}{\tau_{+}\log\;\frac{v_{S} + q_{+}}{v + q_{+}}} & {{{if}\mspace{14mu} v} > {\hat{v}}_{+}} \\\infty & {otherwise}\end{matrix} \right.} & (14)\end{matrix}$where {circumflex over (v)}₊ is typically set to parameter v₊, althoughother variations may be possible.

The above definitions of the model dynamics depend on whether the modelis in the positive or negative regime. As mentioned, the coupling andthe regime ρ may be computed upon events. For purposes of statepropagation, the regime and coupling (transformation) variables may bedefined based on the state at the time of the last (prior) event. Forpurposes of subsequently anticipating spike output time, the regime andcoupling variable may be defined based on the state at the time of thenext (current) event.

There are several possible implementations of the Cold model, andexecuting the simulation, emulation or model in time. This includes, forexample, event-update, step-event update, and step-update modes. Anevent update is an update where states are updated based on events or“event update” (at particular moments). A step update is an update whenthe model is updated at intervals (e.g., 1 ms). This does notnecessarily utilize iterative methods or Numerical methods. Anevent-based implementation is also possible at a limited time resolutionin a step-based simulator by only updating the model if an event occursat or between steps or by “step-event” update.

Knowledge-Graph Biased Classification for Robustness to Severely NoisyData

A conventional object recognition system includes an image preprocessingstage, a feature extraction stage, and a classification stage.Specifically, in the conventional object recognition system, the imagepreprocessing stage is specified to preprocess an image and segmentfeatures within the image.

In the present application, segmentation refers to determiningboundaries around objects in an image. For example, an image may includea chair, a desk, and a lamp. Each of these objects may be segmented. Thesegment for each of the aforementioned objects may be, for example, thesmallest rectangle that encloses all the pixels that belong to thatobject.

After preprocessing the image, a feature extraction stage extractsfeatures from the preprocessed image. In the present application, thefeatures may be referred to as objects, such as faces, monitors,keyboards, and/or other objects that may be photographed. Moreover,after extracting the features, the classifier may classify the extractedfeatures. That is, the classification applies one or more possibleclasses to each extracted object. It should also be noted that in thepresent application, classes may be referred to as labels or categories.Additionally, or alternatively, the classifier is specified to classifythe entire image or subsets of the image based on the extractedfeatures. For example, the image may be classified as a sunset.

FIG. 5 illustrates an example of an image 500 that may be classified viaan object recognition system. As shown in FIG. 5, the image 500 includesa PC monitor 506, a keyboard 504, and a mouse 502. Thus, based on thestages of the conventional object recognition system, the extractedobjects are the PC monitor 506, the keyboard 504, and the mouse 502. Asan example, the inferred classes for the extracted PC monitor object maybe PC monitor, TV, and/or window. As another example, the inferredclasses for the extracted keyboard object may be keyboard, tray, andplacemat.

Furthermore, the classifier provides a confidence metric for one or moreclasses inferred from each object. The confidence metric may be based ontraining provided to the network. In the present application, theconfidence metric may be referred to as the confidence, the confidencescore, and/or the confidence value. In one example, an object extractedfrom the feature extractor may be a PC monitor and the classifier outputfor the PC monitor may be: PC monitor: 50%, TV: 40%, window: 10%. Thatis, the network has a 50% confidence that the object is a PC monitor, a40% confidence that the object is a TV, and a 10% confidence that theobject is a window. In this example, the PC monitor, TV, and window areclasses inferred from an object extracted from an image.

Additionally, in one configuration, the difference between the highestconfidence and the second highest confidence may be used as a confusionmetric Specifically, in this configuration, a confusion metric, theconfusion is reversely correlated to the difference between twoconfidence metrics. That is, a low difference between two confidencescores results in a high confusion. For example, in the previousexample, the difference between the confidence metrics for the PCmonitor (50%) and the TV (40%) is less than the difference between thedifference between the confidence metrics for the PC monitor (50%) andthe window (10%). Thus, in the previous example, there is a greaterconfusion as to whether the extracted object is a PC monitor or a TV incomparison to the confusion as to whether the extracted object is a PCmonitor or a window.

In one configuration, for a given object, the confidences for eachpredicted class, in descending order, are P1, P2, . . . , Pk for kpredictions. Additionally, or alternatively, the confusion metric may bedetermined as follows:Classifier confusion value=P ₁ −P ₂   (15)Classifier confusion value=(P ₁ −P ₂)/(P ₁ +P ₂)   (16)Classifier confusion value=P ₁/median(P ₁ , P ₂ , . . . , P _(k))   (17)Classifier confusion value=P ₁/mean(P ₁ , P ₂ , . . . , P _(k))   (18)

In equations 15-18 the classifier confusion value is inverselycorrelated to the confusion. That is, a lower classifier confusion valuemay indicate a high confusion.

As previously discussed, the metrics may be based on training providedto the network. Thus, in some cases, a low confidence may be given to anobject if the network has not been trained on the object. Additionally,or alternatively, the wrong class may receive the highest confidencescore. In some cases, low image quality, occlusions, bad segmentation ofthe object, and/or other factors may cause a high confidence metric tobe incorrectly assigned to a class. For example, as shown in FIG. 5,only a partial image of the PC monitor is captured. Thus, in the exampleof FIG. 5, the object recognition system may assign a higher confidenceto a wrong class, such as window, in comparison to the confidenceassigned to the correct class, such as PC monitor.

TABLE 1 provides an example of confidences assigned to classes inferredfor objects extracted from an image. In this example, the confidencemetrics of TABLE 1 may be based on the example of FIG. 5 in which the PCmonitor 506 is partially shown in the image. As shown in TABLE 1, theclassifier output for the monitor is: Window: 40%, Monitor: 35%, TV:25%. That is, the network infers that the extracted PC monitor object iseither a window, monitor, or TV. Furthermore, the network has a 40%confidence that the extracted PC monitor object is a window, 35%confidence that the extracted PC monitor object is a monitor, and a 25%confidence that the extracted PC monitor object is a TV.

TABLE 1 True Object Inference 1 Inference 2 Inference 3 Monitor Window -40% Monitor - 35% TV - 25% CPU CPU - 80% Box - 15% Toaster - 5% MouseMouse - 90% Insect - 10% Keyboard Keyboard - 70% Tray - 20% Placemat -10%

For TABLE 1, the network has not considered the probability that theextracted PC monitor object is a PC monitor when there is a high degreeof confidence that other three objects are a CPU (not shown in FIG. 5),a mouse, and a keyboard. Therefore, in the example of TABLE 1, theconfidence that the object is a window is greater in comparison to theconfidence that the object is a monitor.

In most cases, a human observer may accurately determine an object thatis obstructed in an image based on the association of the object withun-obstructed objects of the image. For example, a human observer mayrecognize an obstructed monitor based on an association of monitors withun-obstructed objects, such as a CPU, a mouse, and/or a keyboard. Theassociation knowledge is based on associative maps for objects thatcommonly occur with other objects.

Aspects of the present disclosure are directed to generatingknowledge-graphs that indicate a probability of co-existence ofdifferent objects in an environment. Additionally, aspects of thepresent disclosure are also directed to biasing output of a classifierbased on the knowledge-graph. Moreover, aspects of the presentdisclosure are further directed to updating an existing knowledge-graphbased on the output of a classifier.

It should be noted that conventional systems may build Bayesian beliefnetworks. For example, hierarchical databases may be built onknowledge-graphs derived from an English lexical dictionary. Moreover,other conventional systems improve classification results with prolongeduse. Still, the conventional systems do not use a knowledge-graph basedclassification system that is dynamically-learned during training of thenetwork.

Aspects of the present disclosure provide flexibility by decoupling aclassifier from a parallel polling mechanism. Additionally, oralternatively, the parallel polling mechanism is coupled to theclassifier to modify classifier weights based on the knowledge-graphoutput.

In one configuration, the knowledge-graph based classification systemtags people (or objects) that are occluded and/or turned away from thecamera. In some cases, a user may tend to take pictures with one or moreindividuals from a specific group, such as friends and/or family. Overtime, the knowledge-graph associates the user with specific individuals.That is, the knowledge-graph biases the association with specificindividuals based on pictures taken over time. Therefore, in oneconfiguration, based on the association, a specific individual in agroup image is tagged according to the knowledge-graph bias for theassociation of the specific individual with other individuals in theimage. More specifically, the individual may be tagged even when theimage of the individual is obstructed and/or noisy.

In another configuration, the knowledge-graph based classificationsystem tags fast moving images captured via an image capturing device,such as a head-mounted display. Due to the nature of some imagecapturing devices, such as the head-mounted display, some capturedimages may be partially in view of the camera and/or may only be in viewof the camera for a small time period. That is, some of the capturedimages may not be well framed.

Therefore, in this configuration, probable tags for objects that arepartially in the view of the image capturing device and/or outside theview of the image capturing device are obtained by the knowledge-graph.It should be noted that the object not in the view of the imagecapturing device may be in a relative environment of the user. Theknowledge-graph may be specified for the image capturing device toreduce the search space of possible object classes. Aspects of thepresent disclosure are not limited to head-mounted displays and are alsocontemplated for other image capturing devices.

In another configuration, the knowledge-graph based classificationsystem suggests people to include in a photograph. Specifically, theknowledge-graph may build graphs of groups of people that commonlyappear in captured images. Thus, in one configuration, while framing agroup photo, the knowledge-graph provides contextual information forother individuals that should be included in the photo based on theknowledge-graph information.

In some cases, certain classes may not be identified by the classifier.Therefore, in one configuration, the knowledge-graph basedclassification system presents a list of possible classes in anobject-tagging application. That is, the knowledge-graph may aid theuser to apply the correct class to an extracted object. For example, ina face tagging application, one or more faces may have a low confidenceor may not be segmented. Thus, in this example, the knowledge-graphprovides a list of possible names that the user can associate with aspecific individual. Moreover, the classifier may be trained from theuser-tagged images and/or the knowledge-graph may be updated based onthe user-tagged images.

FIG. 6 illustrates an example of a knowledge-graph based on an aspect ofthe present disclosure. As shown in FIG. 6, each object is associatedwith another object. In this example, as shown in FIG. 6, the thickerlines represent stronger probabilities of co-existence. For example, asshown in FIG. 6, the monitor has a stronger probability of co-existingwith a mouse in comparison to the probability of co-existing with awindow. In another example, as shown in FIG. 6, the DVD has a strongerprobability of co-existing with the TV in comparison to the probabilityof co-existing with the window.

In one configuration, the knowledge-graph is represented as a matrix ofpair-wise occurrence probabilities. For example, the knowledge-graph ofFIG. 6 may be coded as the knowledge-graph matrix (KGM) of TABLE 2.

TABLE 2 Monitor TV Window DVD Mouse Monitor 1 0 0.2 0.4 0.9 TV 0 1 0.60.9 0.7 Window 0.2 0.6 1 0.6 0 DVD 0.4 0.9 0.6 1 0 Mouse 0.9 0.7 0 0 1

As shown in TABLE 2, each intersection of a column and row representsthe occurrence probability of two objects. For example, as shown inTABLE 2, the monitor has a zero probability of occurring with a TV, a0.2 probability of occurring with the window, a 0.4 probability ofoccurring with the DVD, and a 0.9 probability of occurring with themouse.

The pseudo-code provided below is an example for generating theknowledge-graph from an initial database without external input. Thatis, the knowledge-graph may be trained based on acquired images. In thepresent example, A_(i) refers to images, L₁ . . . L_(N) refer to classesor objects, and F refers to refers to the amount of increment for agiven matrix location defined by (L_(i), L_(j)). In one configuration, Fis a constant scalar value

-   Initialize all elements of the knowledge-graph matrix (KGM)-   For each {image in database, A_(i)}

Get label indices of objects: L₁, L₂, . . . , L_(N)

Increment matrix entries (L_(i), L_(j)), {0<i, j<=N} by F

In this configuration, it is assumed that each image is annotated interms of the classes. Furthermore, it is assumed that each class has aunique index. For example, a class, such as car, has an index of 7 andanother class, such as traffic light, has an index of 31. Thus, thematrix elements (7, 31) and (31, 7) for the two classes include theprobability of co-existence of the two classes, such as the car andtraffic light. The co-existence may also be referred to as co-occurrenceand vice versa.

It should be noted that aspects of the present disclosure assume anon-directed graph, resulting in a symmetric knowledge-graph matrix.Still, aspects of the present disclosure are also contemplated for atemporal sequence. That is, in some cases, such as speech/audio datehaving n-tuple models, the order of appearance is used for determining aco-existence probability. In another configuration, the dimensionalityof the knowledge-graph matrix is greater than two so that the dimensionscapture co-occurrence statistics for more than two objects at a timeand/or associates other environmental factors, such as GPS locationand/or time of day, to the co-occurrence statistics of multiple objects.

Based on the previous example, the matrix element (7, 31) may indicatethe probability of one object, such as a car, being followed by anotherobject, such as a traffic light, in one or more images, such as a video.Additionally, the matrix element (31, 7) may indicate the probability ofone object, such as the traffic light, being followed by another object,such as the car, in one or more images, such as a video. In the presentexample of a directed graph, a third dimension may be added to indicatea time between co-occurrences. For example, a matrix element (7, 31, 10)may indicate the probability of a car followed by a traffic light withinten time units of each other in a video segment. The time units may beseconds, minutes, hours, or any other unit of time.

In the present example, a parallel scoring system is specified for agiven class. That is, when an object is presented to the system,confidence metrics P₁, P₂, . . . P_(k) for the top k predicted classesare specified for the object. For example, as previously discussed, inTABLE 1, a metric, such as the confidence metric, for the monitor objectmay be 40% (P₁), 35% (P₂), and 25% (P₃).

In one configuration, the system determines the object with the greatestconfidence metric P₁. For example, based on TABLE 1, the mouse has thegreatest confidence metric (90%). Furthermore, for each object in theimage, such as the mouse, CPU, keyboard, and monitor, the systemdetermines a metric. In one configuration, the system determines aconfusion metric based on a difference of the confidences of theclasses. A confusion metric may be based on the following equation:Classifier confusion value=1/(P ₁ −P ₂)   (19)

As shown in TABLE 1, the system has a 40% confidence that the monitor isa window and a 35% confidence that the monitor is a monitor. Therefore,based on equation 15, the confusion metric is 20%. That is the confusionmetric is the quotient of the difference between two confidence metricsdivided by a numerator having a value of 1.

In this configuration, a high confusion metric indications thatconfusion exists as to whether a true object is one of two inferredclasses. Thus, for an object in the image, if the confusion metric isless than a threshold, the predicted class is accepted as the correctclass. Moreover, if the confusion metric for an object is greater thanthe threshold, the system determines the probability that bindspredicted classes for object to the class with the highest confidence.

In the present example, the threshold may be 10%. Therefore, for TABLE1, because the classifier confusion for the monitor is 20%, the systemdoes not accept the predicted class having the highest confidence value,such as the window, as the correct class. Thus, the system determinesthe probability that binds predicted classes for an object, such as themonitor, to the object with the highest confidence P₁, such as themouse.

The bindings may be based on the knowledge-graph matrix probabilitiesW₁, W₂, . . . W_(k) of TABLE 2. After determining the knowledge-graphmatrix probabilities, a weighted confidence is determined based on aproduct of the confidence metrics P₁, P₂, . . . P_(k) of the object andthe knowledge-graph matrix probabilities W₁, W₂, . . . W_(k). Finally,the weighted confidence metrics of the predicted classes for an objectare used to select the predicted class having the highest weightedconfidence.

In the present example, based on TABLE 2, when the object with thehighest confidence is a mouse, the weight (W₁) for the window is 0 andthe weight (W₂) for the monitor is 0.9. The knowledge-graph matrix fromTABLE 2 determines the weighted confidence score of the classes for themonitor. For example, for the monitor, the weighted confidence of thewindow class is the product of the original confidence P₁ (40%) and theweight W₁ (0). Additionally, the weighted confidence of the monitorclass is the product of the original confidence P₂ (35%) and the weightW₂ (0.9). The weighted confidences based on TABLE 1 and TABLE 2 areshown in TABLE 3.

TABLE 3 True Object Inference 1 Inference 2 Inference 3 Monitor Window -0% Monitor - 32% TV - 17%

In this example, the confidence for the window, monitor and TV areweighted with the knowledge-graph matrix probabilities linking the mouseto the window, the mouse to the monitor, and the mouse to the TV. Basedon the new confidence scores, the monitor is now considered the winningclass because the monitor has the highest weighted confidence.

That is, the output of the classifier is biased based on the weightsprovided in the knowledge-graph matrix. For example, as shown in TABLE2, it is more likely for a mouse to co-exist with a monitor, rather thanco-existing with either a TV or a window. Therefore, the mouse/monitorrelationship is given a greater weight in comparison to the mouse/windowrelationship and the mouse/TV relationship. The weights provided in theknowledge-graph matrix may be based on the training of the system.

In one configuration, the output of the deep convolutional network (DCN)classifier also augments the knowledge-graph matrix. In thisconfiguration, based on the output of the classifier, the system selectsall objects with a confusion that is less than a threshold and buildsthe knowledge-graph matrix from the selected objects.

For example, TABLE 1 indicates that CPU, mouse, and keyboard have lowconfusion metrics. Therefore, the entries, such as CPU, mouse, andkeyboard, may increment the entries in the knowledge-graph matrix bylinking the object labels in a pairwise form. The increment amount maybe fixed or may be a function of the amount of confusion for thepairwise participant class labels. That is, the objects with a confusionthat is below the threshold may update the weights of theknowledge-graph matrix. That is, the system may be trained by updatingthe weights of the knowledge-graph matrix.

Alternatively, or in addition, a user may correct a class assigned to anobject when the inference is incorrect. In this configuration, thecorrect class is assigned a confidence of 100% and the correspondingentries in the knowledge-graph matrix that link the class of thecorrected object to other objects are incremented. In anotherconfiguration, the knowledge-graph matrix is updated with negativecorrelations, such as leaky updates.

Leaky updates specify a decay for each element of the knowledge-graphmatrix over time in consideration of the loss of associativity overtime. For example, if a person does not associate with members of acertain group of friends, the knowledge-graph matrix will reflect theloss of association over time by reducing the corresponding entries inthe knowledge-graph matrix.

As previously discussed, spatial correlations may also be used formultiple dimensional knowledge-graph matrices. In one aspect, anotherdimension, such as a third or fourth dimension, may be specified toindicate the spatial separation of objects. For example, element (7, 31,10) may indicate the probability of co-occurrence of a car and a trafficlight within ten distance units of each other. The distance units may befeet, inches, meters, or any other measurement unit.

Thus, based on aspects of the present disclosure, the knowledge-graphmatrix uses an initial dataset and also adapts to future images based onthe probability of co-occurrence of different object classes. In oneconfiguration, the spatial and/or temporal relations of objects areconsidered when objects are labeled for confidence. Moreover, aspects ofthe present disclosure may also be applied to speech and/or video. Forexample, video frames may be repopulated based on contents of a previousframe.

In one configuration, the feature extraction is implemented as a deepconvolutional network. In this configuration, an integrated deepconvolutional network knowledge-graph matrix is specified by connectingall nodes in the top layer of the deep convolutional network withplastic synapses. That is, each node represents a class and theactivation of that node is the probability of a certain class. When theknowledge-graph is learned, synapses between co-occurring objects' nodesare strengthened and synapses between non-co-occurring objects' nodesare weakened. The lateral synapses add input to an object's node basedon the other activated object nodes. Specifically, the knowledge-graphmatrix is implemented via lateral connections in the output layer of thedeep convolutional network. The plasticity of the synapses allows theuser to update the knowledge-graph matrix or new object co-occurrencesto be learned over time. In one configuration, two separate sets ofsynapses are provided, one for the knowledge-graph matrix and one forthe classifier.

Aspects of the present disclosure improve performance of aclassification network in the presence of noise, reduce latency ofclassification by pre-fetching possible output classes and reducing thesearch-space, and enable novel user-experiences.

FIG. 7 illustrates an example implementation 700 of the aforementionedobject classification based on knowledge-graph association using ageneral-purpose processor 702 in accordance with certain aspects of thepresent disclosure. Variables (neural signals), synaptic weights, systemparameters associated with a computational network (neural network),delays, and frequency bin information may be stored in a memory block704, while instructions executed at the general-purpose processor 702may be loaded from a program memory 706. In an aspect of the presentdisclosure, the instructions loaded into the general-purpose processor702 may comprise code for building a knowledge-graph that computespair-wise probabilities of co-occurrence of different object classes,using the knowledge-graph to augment the performance of an objectclassification system in the presence of noise, and/or adapting theknowledge-graph with new data from the output of the classifier or fromuser generated tags.

FIG. 8 illustrates an example implementation 800 of the aforementionedobject classification based on knowledge-graph association where amemory 802 can be interfaced via an interconnection network 804 withindividual (distributed) processing units (neural processors) 808 of acomputational network (neural network) in accordance with certainaspects of the present disclosure. Variables (neural signals), synapticweights, system parameters associated with the computational network(neural network) delays, frequency bin information, knowledge-graphassociations may be stored in the memory 802, and may be loaded from thememory 802 via connection(s) of the interconnection network 804 intoeach processing unit (neural processor) 808. In an aspect of the presentdisclosure, the processing unit 808 may be configured to build aknowledge-graph that computes pair-wise probabilities of co-occurrenceof different object classes, use the knowledge-graph to augment theperformance of an object classification system in the presence of noise,and/or adapt the knowledge-graph with new data from the output of theclassifier or from user generated tags.

FIG. 9 illustrates an example implementation 900 of the aforementionedobject classification based on knowledge-graph association. Asillustrated in FIG. 9, one memory bank 902 may be directly interfacedwith one processing unit 904 of a computational network (neuralnetwork). Each memory bank 902 may store variables (neural signals),synaptic weights, and/or system parameters associated with acorresponding processing unit (neural processor) 904 delays, frequencybin information, and knowledge-graph associations. In an aspect of thepresent disclosure, the processing unit 904 may be configured to build aknowledge-graph that computes pair-wise probabilities of co-occurrenceof different object classes, to use the knowledge-graph to augment theperformance of an object classification system in the presence of noise,and/or to adapt the knowledge-graph with new data from the output of theclassifier or from user generated tags.

FIG. 10 illustrates an example implementation of a neural network 1000in accordance with certain aspects of the present disclosure. Asillustrated in FIG. 10, the neural network 1000 may have multiple localprocessing units 1002 that may perform various operations of methodsdescribed above. Each local processing unit 1002 may comprise a localstate memory 1004 and a local parameter memory 1006 that storeparameters of the neural network. In addition, the local processing unit1002 may have a local (neuron) model program (LMP) memory 1010 forstoring a local model program, a local learning program (LLP) memory1010 for storing a local learning program, and a local connection memory1012. Furthermore, as illustrated in FIG. 10, each local processing unit1002 may be interfaced with a configuration processor unit 1014 forproviding configurations for local memories of the local processingunit, and with a routing connection processing unit 1016 that providerouting between the local processing units 1002.

In one configuration, a neuron model is configured for classifying anobject based on a knowledge-graph association. The neuron model includesan applying means, and a determining means. In one aspect, the applyingmeans, and/or determining means may be the general-purpose processor702, program memory 706, memory block 704, memory 802, interconnectionnetwork 804, processing units 808, processing unit 904, local processingunits 1002, and or the routing connection processing units 1016configured to perform the functions recited. In another configuration,the aforementioned means may be any module or any apparatus configuredto perform the functions recited by the aforementioned means.

According to certain aspects of the present disclosure, each localprocessing unit 1002 may be configured to determine parameters of theneural network based upon desired one or more functional features of theneural network, and develop the one or more functional features towardsthe desired functional features as the determined parameters are furtheradapted, tuned and updated.

FIG. 11 illustrates a method 1100 for classifying objects. In block1102, an object classifier applies one or more confidence scores toobjects. That is, each object is associated with one or more confidencescores. Furthermore, in block 1104, the object classifier determines ametric based on the confidence scores. Finally, in block 1106, theobject classifier determines a classification of a first object based ona knowledge-graph when the metric is above the threshold.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and generalprocessing, including the execution of software stored on themachine-readable media. The processor may be implemented with one ormore general-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Software shall be construed broadly to meaninstructions, data, or any combination thereof, whether referred to assoftware, firmware, middleware, microcode, hardware descriptionlanguage, or otherwise. Machine-readable media may include, by way ofexample, random access memory (RAM), flash memory, read only memory(ROM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable programmable Read-onlymemory (EEPROM), registers, magnetic disks, optical disks, hard drives,or any other suitable storage medium, or any combination thereof. Themachine-readable media may be embodied in a computer-program product.The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or general register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured as a general-purpose processingsystem with one or more microprocessors providing the processorfunctionality and external memory providing at least a portion of themachine-readable media, all linked together with other supportingcircuitry through an external bus architecture. Alternatively, theprocessing system may comprise one or more neuromorphic processors forimplementing the neuron models and models of neural systems describedherein. As another alternative, the processing system may be implementedwith an application specific integrated circuit (ASIC) with theprocessor, the bus interface, the user interface, supporting circuitry,and at least a portion of the machine-readable media integrated into asingle chip, or with one or more field programmable gate arrays (FPGAs),programmable logic devices (PLDs), controllers, state machines, gatedlogic, discrete hardware components, or any other suitable circuitry, orany combination of circuits that can perform the various functionalitydescribed throughout this disclosure. Those skilled in the art willrecognize how best to implement the described functionality for theprocessing system depending on the particular application and theoverall design constraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a generalregister file for execution by the processor. When referring to thefunctionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. In addition, anyconnection is properly termed a computer-readable medium. For example,if the software is transmitted from a website, server, or other remotesource using a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects computer-readable media maycomprise non-transitory computer-readable media (e.g., tangible media).In addition, for other aspects computer-readable media may comprisetransitory computer-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of computer-readablemedia.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method for computer-implemented tagging ofobjects in an image, the method comprising: processing the image by acomputer-implemented artificial neural network to identify at least afirst and a second object and generate, for at least the first object,at least a first and a second predicted classification and correspondingconfidence values, the first object being partially visible in theimage; generating an adjusted first confidence value by adjusting theconfidence value corresponding to the first predicted classificationbased on a predetermined co-existence probability for the firstpredicted classification and the second object and also at least one ofa physical location of an image capturing device or a time when theimage was captured; generating an adjusted second confidence value byadjusting the confidence value corresponding to the second predictedclassification based on a predetermined co-existence probability for thesecond predicted classification and the second object and also at leastone of the physical location of the image capturing device or the timewhen the image was captured; determining a classification for the firstobject based on the first and second predicted classifications and thecorresponding adjusted first and second confidence values; andgenerating a tag for the first object using the determinedclassification.
 2. The method of claim 1, wherein the predeterminedco-existence probabilities are obtained from a knowledge-graph matrix.3. The method of claim 2, further comprising updating theknowledge-graph matrix based on the classification of the first object.4. The method of claim 3, wherein the updating is based on user input.5. The method of claim 1, further comprising calculating a classifierconfusion value based on a difference between the confidence valuescorresponding to the first and second predicted classifications.
 6. Themethod of claim 5, wherein the steps of generating adjusted confidencevalues are performed only if the classifier confusion value is greaterthan a threshold.
 7. The method of claim 1, wherein the image is a frameof a video stream.
 8. A computer apparatus comprising a memory and aprocessor coupled to the memory, wherein the processor is adapted to:process an image by a computer-implemented artificial neural network toidentify at least a first and a second object and generate, for at leastthe first object, at least a first and a second predicted classificationand corresponding confidence values, the first object being partiallyvisible in the image; generate an adjusted first confidence value byadjusting the confidence value corresponding to the first predictedclassification based on a predetermined co-existence probability for thefirst predicted classification and the second object and also at leastone of a physical location of an image capturing device or a time whenthe image was captured; generate an adjusted second confidence value byadjusting the confidence value corresponding to the second predictedclassification based on a predetermined co-existence probability for thesecond predicted classification and the second object and also at leastone of the physical location of the image capturing device or the timewhen the image was captured; determine a classification for the firstobject based on the first and second predicted classifications and thecorresponding adjusted first and second confidence values; and generatea tag for the first object using the determined classification.
 9. Theapparatus of claim 8, wherein the predetermined co-existenceprobabilities are obtained from a knowledge-graph matrix.
 10. Theapparatus of claim 9, wherein the processor is further adapted to updatethe knowledge-graph matrix based on the classification of the firstobject.
 11. The apparatus of claim 10, wherein the updating is based onuser input.
 12. The apparatus of claim 8, wherein the processor isfurther adapted to calculate a classifier confusion value based on adifference between the confidence values corresponding to the first andsecond predicted classifications.
 13. The apparatus of claim 12, whereinthe processor is adapted to generate the adjusted confidence values onlyif the classifier confusion value is greater than a threshold.
 14. Theapparatus of claim 8, wherein the image is a frame of a video stream.15. A non-transitory computer-readable medium having program coderecorded thereon for computer-implemented tagging of objects in animage, the program code being executed by a processor and comprising:program code to process the image by a computer-implemented artificialneural network to identify at least a first and a second object andgenerate, for at least the first object, at least a first and a secondpredicted classification and corresponding confidence values, the firstobject being partially visible in the image; program code to generate anadjusted first confidence value by adjusting the confidence valuecorresponding to the first predicted classification based on apredetermined co-existence probability for the first predictedclassification and the second object and also at least one of a physicallocation of an image capturing device or a time when the image wascaptured; program code to generate an adjusted second confidence valueby adjusting the confidence value corresponding to the second predictedclassification based on a predetermined co-existence probability for thesecond predicted classification and the second object and also at leastone of the physical location of the image capturing device or the timewhen the image was captured; program code to determine a classificationfor the first object based on the first and second predictedclassifications and the corresponding adjusted first and secondconfidence values; and program code to generate a tag for the firstobject using the determined classification.